

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_     :    Optional[Any]     		=   logging.get_logger(__name__)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      OrderedDict()
 for key, value in state_dict.items():
  if key.startswith('module.encoder'						):
   SCREAMING_SNAKE_CASE		    =      key.replace('module.encoder'		,				'glpn.encoder'						)
  if key.startswith('module.decoder'						):
   SCREAMING_SNAKE_CASE		    =      key.replace('module.decoder'		,				'decoder.stages'						)
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   SCREAMING_SNAKE_CASE		    =      key[key.find('patch_embed'						) + len('patch_embed'						)]
   SCREAMING_SNAKE_CASE		    =      key.replace(f"""patch_embed{idx}"""		,				f"""patch_embeddings.{int(a__						)-1}"""						)
  if "norm" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('norm'		,				'layer_norm'						)
  if "glpn.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   SCREAMING_SNAKE_CASE		    =      key[key.find('glpn.encoder.layer_norm'						) + len('glpn.encoder.layer_norm'						)]
   SCREAMING_SNAKE_CASE		    =      key.replace(f"""layer_norm{idx}"""		,				f"""layer_norm.{int(a__						)-1}"""						)
  if "layer_norm1" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('layer_norm1'		,				'layer_norm_1'						)
  if "layer_norm2" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('layer_norm2'		,				'layer_norm_2'						)
  if "block" in key:
   # replace for example block1 by block.0
   SCREAMING_SNAKE_CASE		    =      key[key.find('block'						) + len('block'						)]
   SCREAMING_SNAKE_CASE		    =      key.replace(f"""block{idx}"""		,				f"""block.{int(a__						)-1}"""						)
  if "attn.q" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('attn.q'		,				'attention.self.query'						)
  if "attn.proj" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('attn.proj'		,				'attention.output.dense'						)
  if "attn" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('attn'		,				'attention.self'						)
  if "fc1" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('fc1'		,				'dense1'						)
  if "fc2" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('fc2'		,				'dense2'						)
  if "linear_pred" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('linear_pred'		,				'classifier'						)
  if "linear_fuse" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('linear_fuse.conv'		,				'linear_fuse'						)
   SCREAMING_SNAKE_CASE		    =      key.replace('linear_fuse.bn'		,				'batch_norm'						)
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   SCREAMING_SNAKE_CASE		    =      key[key.find('linear_c'						) + len('linear_c'						)]
   SCREAMING_SNAKE_CASE		    =      key.replace(f"""linear_c{idx}"""		,				f"""linear_c.{int(a__						)-1}"""						)
  if "bot_conv" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('bot_conv'		,				'0.convolution'						)
  if "skip_conv1" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('skip_conv1'		,				'1.convolution'						)
  if "skip_conv2" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('skip_conv2'		,				'2.convolution'						)
  if "fusion1" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('fusion1'		,				'1.fusion'						)
  if "fusion2" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('fusion2'		,				'2.fusion'						)
  if "fusion3" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('fusion3'		,				'3.fusion'						)
  if "fusion" in key and "conv" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('conv'		,				'convolutional_layer'						)
  if key.startswith('module.last_layer_depth'						):
   SCREAMING_SNAKE_CASE		    =      key.replace('module.last_layer_depth'		,				'head.head'						)
  SCREAMING_SNAKE_CASE		    =      value

 return new_state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Tuple						)		->				str:
 '''simple docstring'''


 for i in range(config.num_encoder_blocks						):
  for j in range(config.depths[i]						):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   SCREAMING_SNAKE_CASE		    =      state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""						)
   SCREAMING_SNAKE_CASE		    =      state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""						)
   # next, add keys and values (in that order) to the state dict
   SCREAMING_SNAKE_CASE		    =      kv_weight[
       : config.hidden_sizes[i], :
   ]
   SCREAMING_SNAKE_CASE		    =      kv_bias[: config.hidden_sizes[i]]
   SCREAMING_SNAKE_CASE		    =      kv_weight[
       config.hidden_sizes[i] :, :
   ]
   SCREAMING_SNAKE_CASE		    =      kv_bias[config.hidden_sizes[i] :]


def 							__lowerCAmelCase     (							)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
 SCREAMING_SNAKE_CASE		    =      Image.open(requests.get(a__		,				stream=a__						).raw						)

 return image


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Tuple=False		,				_UpperCamelCase		:	List[Any]=None						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]		,				decoder_hidden_size=64		,				depths=[3, 8, 27, 3]						)

 # load image processor (only resize + rescale)
 SCREAMING_SNAKE_CASE		    =      GLPNImageProcessor()

 # prepare image
 SCREAMING_SNAKE_CASE		    =      prepare_img()
 SCREAMING_SNAKE_CASE		    =      image_processor(images=a__		,				return_tensors='pt'						).pixel_values

 logger.info('Converting model...'						)

 # load original state dict
 SCREAMING_SNAKE_CASE		    =      torch.load(a__		,				map_location=torch.device('cpu'						)						)

 # rename keys
 SCREAMING_SNAKE_CASE		    =      rename_keys(a__						)

 # key and value matrices need special treatment
 read_in_k_v(a__		,				a__						)

 # create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE		    =      GLPNForDepthEstimation(a__						)
 model.load_state_dict(a__						)
 model.eval()

 # forward pass
 SCREAMING_SNAKE_CASE		    =      model(a__						)
 SCREAMING_SNAKE_CASE		    =      outputs.predicted_depth

 # verify output
 if model_name is not None:
  if "nyu" in model_name:
   SCREAMING_SNAKE_CASE		    =      torch.tensor(
       [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]						)
  elif "kitti" in model_name:
   SCREAMING_SNAKE_CASE		    =      torch.tensor(
       [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						)
  else:
   raise ValueError(f"""Unknown model name: {model_name}"""						)

  SCREAMING_SNAKE_CASE		    =      torch.Size([1, 4_80, 6_40]						)

  assert predicted_depth.shape == expected_shape
  assert torch.allclose(predicted_depth[0, :3, :3]		,				a__		,				atol=1e-4						)
  print('Looks ok!'						)

 # finally, push to hub if required
 if push_to_hub:
  logger.info('Pushing model and image processor to the hub...'						)
  model.push_to_hub(
      repo_path_or_name=Path(a__		,				a__						)		,				organization='nielsr'		,				commit_message='Add model'		,				use_temp_dir=a__		,				)
  image_processor.push_to_hub(
      repo_path_or_name=Path(a__		,				a__						)		,				organization='nielsr'		,				commit_message='Add image processor'		,				use_temp_dir=a__		,				)


if __name__ == "__main__":
      a_     :    List[Any]     		=   argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          default=None,
          type=str,
          help="Path to the original PyTorch checkpoint (.pth file).",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
      )
      parser.add_argument(
          "--model_name",
          default="glpn-kitti",
          type=str,
          help="Name of the model in case you're pushing to the hub.",
      )
      a_     :    Union[str, Any]     		=   parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			with open(a__) as metadata_file:
						a_						:						Any 							= json.load(a__)
			a_						:						Dict 							= LukeConfig(use_entity_aware_attention=a__  ,	**metadata["""model_config"""])

			# Load in the weights from the checkpoint_path
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")

			# Load the entity vocab file
			a_						:						List[str] 							= load_entity_vocab(a__)

			a_						:						int 							= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""])

			# Add special tokens to the token vocabulary for downstream tasks
			a_						:						Optional[Any] 							= AddedToken("""<ent>"""  ,	lstrip=a__  ,	rstrip=a__)
			a_						:						int 							= AddedToken("""<ent2>"""  ,	lstrip=a__  ,	rstrip=a__)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]})
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}''')
			tokenizer.save_pretrained(a__)
			with open(os.path.join(a__  ,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""])  ,	"""w""") as f:
						json.dump(a__  ,	a__)

			a_						:						List[str] 							= LukeTokenizer.from_pretrained(a__)

			# Initialize the embeddings of the special tokens
			a_						:						Optional[int] 							= state_dict["""embeddings.word_embeddings.weight"""]
			a_						:						List[str] 							= word_emb[tokenizer.convert_tokens_to_ids(["""@"""])[0]].unsqueeze(0)
			a_						:						List[Any] 							= word_emb[tokenizer.convert_tokens_to_ids(["""#"""])[0]].unsqueeze(0)
			a_						:						Optional[Any] 							= torch.cat([word_emb, ent_emb, enta_emb])

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers):
						for matrix_name in ["query.weight", "query.bias"]:
									a_						:						Any 							= f'''encoder.layer.{layer_index}.attention.self.'''
									a_						:						List[str] 							= state_dict[prefix + matrix_name]
									a_						:						List[Any] 							= state_dict[prefix + matrix_name]
									a_						:						Dict 							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			a_						:						str 							= state_dict["""entity_embeddings.entity_embeddings.weight"""]
			a_						:						int 							= entity_emb[entity_vocab["""[MASK]"""]]

			a_						:						int 							= LukeModel(config=a__).eval()

			a_							,					a_						:						Optional[int] 							= model.load_state_dict(a__  ,	strict=a__)
			if not (len(a__) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f'''Missing keys {", ".join(a__)}. Expected only missing embeddings.position_ids''')
			if not (all(key.startswith("""entity_predictions""") or key.startswith("""lm_head""") for key in unexpected_keys)):
						raise ValueError(
						    """Unexpected keys"""
						    f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

			# Check outputs
			a_						:						List[Any] 							= LukeTokenizer.from_pretrained(a__  ,	task="""entity_classification""")

			a_						:						str 							= (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			a_						:						str 							= (3_9, 4_2)
			a_						:						Tuple 							= tokenizer(a__  ,	entity_spans=[span]  ,	add_prefix_space=a__  ,	return_tensors="""pt""")

			a_						:						List[str] 							= model(**a__)

			# Verify word hidden states
			if model_size == "large":
						a_						:						Optional[int] 							= torch.Size((1, 4_2, 1_0_2_4))
						a_						:						List[str] 							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
			else:  # base
						a_						:						List[str] 							= torch.Size((1, 4_2, 7_6_8))
						a_						:						str 							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						a_						:						Dict 							= torch.Size((1, 1, 1_0_2_4))
						a_						:						int 							= torch.tensor([[0.0466, -0.0106, -0.0179]])
			else:  # base
						a_						:						Optional[Any] 							= torch.Size((1, 1, 7_6_8))
						a_						:						str 							= torch.tensor([[0.1457, 0.1044, 0.0174]])

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}''')
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(a__))
			model.save_pretrained(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= {}
			with open(a__  ,	"""r"""  ,	encoding="""utf-8""") as f:
						for index, line in enumerate(a__):
									a_							,					a_						:						List[Any] 							= line.rstrip().split("""\t""")
									a_						:						Any 							= index

			return entity_vocab


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)


